Improved algorithm based on S-curve global dynamic dimming

Zheng TONG; Lei WU; Chen ZHAO; Guo-qiang LV

doi:10.3788/YJYXS20183312.1019

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Improved algorithm based on S-curve global dynamic dimming

Image Processing | 更新时间：2020-08-12

- Improved algorithm based on S-curve global dynamic dimming
- Chinese Journal of Liquid Crystals and Displays Vol. 33, Issue 12, Pages: 1019-1025(2018)
- 作者机构：
  
  1.合肥工业大学电子科学与应用物理学院, 安徽合肥 230009
  2.合肥工业大学仪器科学与光电工程学院, 安徽合肥 230009
  3.合肥工业大学特种显示技术国家工程实验室现代显示技术省部共建国家重点实验室光电技术研究院, 安徽合肥 230009
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20183312.1019
  CLC： TP391;TN27
- Received：05 July 2018，
  
  Accepted：07 September 2018，
  
  Published：2018-12
- 稿件说明：
移动端阅览
Zheng TONG, Lei WU, Chen ZHAO, et al. Improved algorithm based on S-curve global dynamic dimming[J]. Chinese journal of liquid crystals and displays, 2018, 33(12): 1019-1025.
DOI：

Zheng TONG, Lei WU, Chen ZHAO, et al. Improved algorithm based on S-curve global dynamic dimming[J]. Chinese journal of liquid crystals and displays, 2018, 33(12): 1019-1025. DOI： 10.3788/YJYXS20183312.1019.

摘要

S曲线全局动态调光算法可以降低LED液晶显示器的功耗，同时能够提高显示图像的静态对比度，但该算法会造成部分图像色彩失真和细节丢失。针对这一问题，本文提出一种图像细节层分离与视觉显著性理论相结合的S曲线改进算法。首先，将原始图像转换至HSV色彩空间进行亮度和色度分离；然后，在图像亮度分量上采用双边滤波得到图像的基础层与细节层，基础层采用S曲线进行动态范围拉伸，实现像素补偿，细节层则运用视觉显著性理论进行分区与权值增强，弥补由像素补偿带来的细节损失；最后，将处理后的各层图像转换至RGB空间显示。将本文算法的仿真结果与原S曲线算法的结果进行对比。结果显示，本文算法在维持原算法功耗降低和静态对比度提升水平不变的基础上，解决了原算法在部分图像中出现的色彩失真和细节丢失问题，提升了图像的视觉显示效果，同时本文算法的仿真结果具有更大的信息熵和平均梯度。

Abstract

The S-curve global dynamic dimming algorithm can reduce the power consumption of the LED liquid crystal display

and at the same time improve the static contrast of the displayed image

but the algorithm will cause partial image color distortion and detail loss. To solve this problem

this paper proposes an improved S-curve algorithm combining image detail layer separation and visual saliency theory. Firstly

the original image is converted to the HSV color space for luminance and chrominance separation; then

the base layer and the detail layer of the image are obtained by bilateral filtering on the image luminance component

and the base layer is subjected to dynamic range stretching using the S curve to realize pixel compensation. The detail layer uses the visual saliency theory to perform partitioning and weight enhancement to compensate for the loss of detail caused by pixel compensation. Finally

the processed layers are converted to RGB space display. The simulation results of the algorithm are compared with the results of the original S-curve algorithm. The results show that the proposed algorithm solves the problem of color distortion and detail loss in some images based on the original algorithm's power consumption reduction and static contrast enhancement level

and improves the visual display effect of the image. The simulation results of the algorithm have greater information entropy and average gradient.

关键词

Keywords

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